public void Embedding()
{
var model = new Sequential();
model.add(new Embedding(1000, 64, input_length: 10));
// the model will take as input an integer matrix of size (batch,
// input_length).
// the largest integer (i.e. word index) in the input should be no larger
// than 999 (vocabulary size).
// now model.output_shape == (None, 10, 64), where None is the batch
// dimension.
var input_array = np.random.randint(1000, size: (32, 10));
model.compile("rmsprop", "mse");
}
public void Test1()
{
var epochs = 200;
var batchSize = 128;
var classes = 10;
var hiddenCount = 128;
var validationSplit = 0.2;
var flatImageSize = 28 * 28;
var mnist = MnistDataset.Read(@"C:\Projects\heightmap_upscale\data\mnist");
var x_train = PrepareImage(mnist.TrainingImages);
var x_test = PrepareImage(mnist.TestImages);
var y_train = PrepareLabels(mnist.TrainingLabels);
var model = new Sequential();
model.add(new Dense(classes, activation: new softmax()));
//model.Compile("SGD", loss: "categorical_crossentropy", metrics: new[] { "accuracy" });
model.compile("SGD", "categorical_crossentropy");
}
